Nail-Based Ngs Data Analysis Method and System for Disease Prediction

This application is a continuation of PCT/KR2024/017050 filed on Nov. 1, 2024, which claims priority to Korean Patent Application No. 10-2023-0151141 filed on Nov. 3, 2023, the entire contents of which are herein incorporated by reference.

The present invention relates to a next generation sequencing (NGS) method and system for disease prediction through the analysis of nail-derived germline mutations and somatic mutations. Specifically, the present invention relates to a nail-based NGS (Next Generation Sequencing) analysis method for the prediction or diagnosis of diseases, comprising the steps of isolating gDNA from a nail and analyzing the isolated gDNA using NGS.

Precision medicine involves identifying individual variations in genetic and environmental factors, medical history, and lifestyle to provide optimized treatment by administering the right drug at the right dose and at the right time for each patient. In particular, as prevention is considered superior to reactive treatment, personal genome analysis serves as a key element in realizing future medical paradigms such as precision and preventive medicine, enabling the preemptive identification and management of specific diseases or health risks.

While large-scale studies such as whole genome sequencing (Whole Genome-seq), transcriptome sequencing (Transcriptome-seq), and epigenome sequencing (Epigenome-seq) are being conducted by various research groups to identify the genetic correlations between complex traits and diseases, several limitations such as nonrandom sampling, disease complexity, the lack of bio big data, and the need for advancements in bioinformatics analysis methods still persist. However, the necessity and utility of personal genome analysis are projected to continue growing, driven by the research achievements in clinical genomics.

Since the COVID-19 crisis, it has been revealed that individuals with underlying conditions such as hypertension and diabetes are more likely to become critically ill upon infection, which has led to increased awareness of routine health management. In particular, as obesity rates rise, chronic diseases accompanied by hypertension are further increasing, and in the case of Korea, most diabetic patients are exhibiting dyslipidemia.

Hyperlipidemia refers to a condition in which blood cholesterol and blood triglyceride levels are abnormally high, and dyslipidemia is a broad concept that includes such abnormalities in blood lipid levels, with one in four adults in Korea currently suffering from hypercholesterolemia and two in five suffering from dyslipidemia. It is reported that hypercholesterolemia is on an increasing trend, with 23% of men and 25% of women having hypercholesterolemia, and while awareness is improving accordingly, three out of ten hypercholesterolemia patients are still unaware of their condition, and although the treatment rate has improved significantly, only about half of hypercholesterolemia patients are actually taking lipid-lowering agents. Although a majority of diabetic patients in Korea have hyperlipidemia, the awareness and treatment rates of these patients are only in the 20-30% range, indicating an urgent need for improvement. It was found that 83.3% of adult diabetic patients had hyperlipidemia, with 88.3% for women and 78.1% for men, showing that women were higher than men, and in particular, the prevalence among the young population aged 19-39 was 88.5%, which was higher than other age groups. This figure is much higher than the hyperlipidemia prevalence (15-20%) of the general population in their 20s and 30s reported in existing studies, suggesting that for diabetic patients, management of hyperlipidemia is necessary from an earlier age (Seung Jae Kim, Oh Deog Kwon, Kyung-Soo Kim, Prevalence, awareness, treatment, and control of dyslipidemia among diabetes mellitus patients and predictors of optimal dyslipidemia control: results from the Korea National Health and Nutrition Examination Survey. Lipids Health Dis. 2021 Mar. 26; 20(1):29.).

Additionally, chronic diseases commonly accompanied by cancer survivors include hypertension, hyperlipidemia, diabetes, osteoporosis, and anemia. Causative factors include genetic factors, lifestyle risk factors, the cancer itself, cancer treatment, and pre-existing chronic diseases, and as a result of analyzing the causes of death of approximately 240,000 long-term cancer survivors who were diagnosed with cancer between 1993 and 2000 and survived for more than five years in Korea, 24% of cancer survivors died from causes other than cancer, most of which were diabetes and cardiovascular diseases (Dong Wook Shin, Young Ho Yun et al., Non-cancer mortality among long-term survivors of adult cancer in Korea: national cancer registry study. Cancer Causes Control 2010; 21: 919-29.). Besides, chronic diseases such as hypertension, hyperlipidemia, osteoporosis, and anemia, which commonly occur at a higher frequency in cancer survivors, need to be checked more frequently than the screening intervals conducted for the general population.

To date, there are no guidelines or instructions on health checkup items for cancer survivors, and general health management guidelines are only being presented for commonly occurring solid cancer types. Cancer survivors have a higher risk of chronic diseases such as obesity, hypertension, diabetes, hyperlipidemia, osteoporosis, and anemia, and secondary cancer than the general population, which varies somewhat by cancer type, and they are a health-vulnerable group for whom smoking cessation, abstinence from alcohol, proper exercise, and nutritional management are very important, necessitating post-management and monitoring for prevention to predict the occurrence of secondary diseases.

Therefore, in order to fundamentally prevent and treat diseases, an approach to identifying genetic status and mutations tailored to individual characteristics is necessary. Sanger-based sequencing, a conventional sequencing method, can generally read DNA fragments within 500-800 base pairs. Thus, it is limited in analyzing long DNA sequences, and its application to research fields requiring large-scale DNA sequence information, such as personal genome analysis, involves disadvantages such as inefficiency in terms of time, labor, and cost. For these reasons, as a new analysis technique capable of obtaining large-scale DNA sequence information with high efficiency and low cost is required, the popularization of next generation sequencing (NGS) and technologies using the same are developing rapidly.

The detection of variations within a genome highly depends on the performance of variant calling software. While various software is currently in use, significant differences exist in the variant calling capabilities of each software. To address this, various studies are being conducted to find an optimal standard pipeline, but the software that demonstrates the best performance may vary depending on the characteristics or quality of the input data.

In particular, Mutect2, which is representatively and widely used among variant detection software, is known to demonstrate excellent performance in low-frequency variants, but cases of false negatives have been reported in clinical settings. Various additional options are provided to overcome this problem, but in some cases, there are limitations in failing to detect clinically important variants. Since software such as Mutect2 uses probabilistic models to calculate the probability of the existence of a variant, there is a possibility that errors may occur during this probability calculation process, which can degrade the reliability of variant detection. Therefore, there is an urgent need for a new approach to increase the accuracy of variant detection and resolve the false negative issue.

As multinational corporations develop NGS equipment and improve reagents, and as bioinformatics techniques advance, the cost of genotyping is gradually becoming more affordable. NGS analysis has various advantages that can replace the conventional Sanger-based methods, and thus it is being utilized in many research, health checkup, and diagnostic service fields.

As a result, tissue and blood samples have traditionally been used as the primary sources of DNA for genetic analysis, but tissue samples have limitations in collection through surgery or procedures, and in the case of blood collection, there are difficulties in collecting specimens due to factors such as arterial thrombosis, skin infection, anemia, or hemophilia patients where blood collection is impossible, persons who have not passed 4 weeks after receiving drugs such as isotretinoin, women during pregnancy or for a certain period after childbirth, and patients with infectious diseases or leukemia.

Additionally, such tissue or blood specimens, upon moving to a place for analysis or testing, specimen transport instability according to temperature changes and high-cost transportation fee burden for maintaining refrigeration or freezing, and freezing or ultra-low temperature equipment and facilities in terms of long-term storage are being required as essential construction items, and due to people such as children or adults or patients who, depending on the case, are suffering from needle phobia or trypanophobia which is a rejection of injection needles, a system that can resolve the above disadvantages from alternative clinical specimens and can screen and diagnose more periodically and smoothly is being required.

In order to solve such problems, the present inventors have completed the present invention by developing an analysis method and system with high accuracy and a reduced possibility of false negatives through nail specimens that can replace blood or tissues.

Seung Jae Kim, Oh Deog Kwon, Kyung-Soo Kim, Prevalence, awareness, treatment, and control of dyslipidemia among diabetes mellitus patients and predictors of optimal dyslipidemia control: results from the Korea National Health and Nutrition Examination Survey. Lipids Health Dis. 2021 Mar. 26; 20(1):29. Dong Wook Shin, Young Ho Yun et al., non-cancer mortality among long-term survivors of adult cancer in Korea: national cancer registry study. Cancer Causes Control 2010; 21: 919-29. Barbitoff, Y. A., Abasov, R., Tvorogova, V. E. et al. Systematic benchmark of state-of-the-art variant calling pipelines identifies major factors affecting accuracy of coding sequence variant discovery. BMC Genomics 23, 155 (2022).

The objective of the present invention is to provide an NGS-based genetic data analysis method and system capable of predicting the presence or absence of a disease according to individual characteristics, which can be easily implemented in health checkup centers, medical institutions, and future DTC (Direct to Consumer) testing services by utilizing nail (fingernail or toenail) specimens.

The present invention provides a nail-based NGS analysis method for predicting or diagnosing a disease and a system for predicting or diagnosing a disease using the same.

Specifically, the present invention provides a nail-based NGS analysis method capable of diagnosing or predicting chronic diseases such as obesity, hyperlipidemia, and diabetes, the occurrence, recurrence, and metastasis of cancer, and a system for predicting or diagnosing a disease using the same.

The present invention provides the nail-based NGS analysis method for predicting or diagnosing the disease, comprising the steps of: (a) isolating gDNA from a nail; and (b) analyzing the gDNA isolated in step (a) by NGS (Next-Generation Sequencing).

The step (a) of isolating gDNA from the nail is a step of preparing a sample for PCR or NGS analysis by applying a gDNA extraction based on a forensic method used in forensic science, rather than applying a conventional general extraction method using phenol/chloroform, in order to reduce DNA damage in the nail specimen.

The nail includes a fingernail, a toenail, or both a fingernail and a toenail. Furthermore, the nail is not limited to those of humans and includes all those of animals (or subjects) having fingernails or toenails, such as dogs and cats.

The growth rate of nails may vary by individual, but on average, they typically grow from 1.8 mm to a maximum of 4.5 mm per month, and a characteristic is that increased stimulation leads to higher blood flow to the nails, activating cell division and resulting in faster growth. Among other relatively non-invasive specimens, hair typically grows at an average rate of 0.3 mm per day, about 1 cm per month, and approximately 12 cm per year; however, this growth rate is slower compared to nails, and in cases of children, individuals without hair, or trace DNA extraction resulting from small hair samples, variables may occur in NGS analysis results. Similarly, in the case of saliva, results can be adversely affected by factors such as the use of mouthwash, foreign substances ingested at the time of collection, excessive water intake, or oral microorganisms; furthermore, it requires a disposable saliva collection kit and a tube containing a separate preservative, and limitations may arise in specific genomic research or diagnosis due to the detection of only trace amounts of DNA. Therefore, the collection of nail specimens which allow for transport and storage at room temperature and have no restrictions regarding age, gender, or specific diseases is the most suitable for analysis.

2 FIG. Through the experimental results shown in, the present inventors confirmed that stable gDNA extraction is possible from nails, just as with other specimens, and the samples were directly collected by the requester at room temperature, stored in paper containers, and then applied to the tests. It was confirmed that nail specimens can be suitable samples for genetic testing as they can be utilized from screening to diagnosis at screening centers and medical institutions by increasing requester convenience in an eco-friendly manner.

As for the collection method, nail samples from individuals or patients can be collected and provided by genetic testing agencies, health checkup centers, clinics, or primary or secondary hospitals in each region. By testing the gDNA (genomic DNA) contained in nail samples, the occurrence of chronic diseases such as aging, obesity, hyperlipidemia, and diabetes, as well as related complications, can be predicted, and the possibility of cancer occurrence, metastasis, and recurrence can be reviewed.

Additionally, the NGS-based data analysis method of the present invention may include a step of obtaining data by receiving existing clinical information from screening agencies or primary to secondary hospitals to enable comparison with existing blood samples of the analysis subject.

For gDNA extraction from nail samples, single nucleotide polymorphism (SNP) genotyping can be performed from nail specimens using phenol/chloroform extraction. As a common method for extracting DNA from various samples including nails, the phenol/chloroform extraction method degrades the cell membranes in the nail sample to release DNA and then extracts the DNA from the solution using salt precipitation.

While phenol/chloroform extraction is a relatively simple and efficient method for extracting DNA from nails, it has the disadvantages of being time-consuming and potentially damaging the DNA. Furthermore, the phenol/chloroform extraction method poses a problem where DNA may also be extracted from other sources, such as bacteria and fungi. This can lead to the contamination of the DNA sample. While the phenol/chloroform extraction method may be used when nail-derived DNA is required only for SNP genotyping, a more efficient and less damaging method is necessary if the DNA is required for other applications such as NGS sequencing or PCR (polymerase chain reaction).

SNP genotyping is a technique used to identify SNPs in DNA samples. SNPs result from variations of a single nucleotide (A, T, C, or G) in a DNA sequence, and they can be used to identify individuals, track genetic variation in populations, and diagnose genetic diseases.

In NGS, if impurities such as salts, phenol, ethanol, or iron are mixed in the sample, or if substances such as heparin, EDTA, NaCl, or KCl are introduced during the DNA extraction stage, the results may be interfered with, leading to inaccurate conclusions; thus, it is necessary to identify the causes of errors before testing.

To reduce such DNA damage, the present invention extracted gDNA by applying a forensic method instead of the extraction method using phenol/chloroform. Accordingly, time-consuming washing steps, such as precipitation using isopropanol or ethanol, are omitted, thereby shortening the extraction time and minimizing the damage rate.

In the NGS analysis step (b), NGS technology refers to a technique that fragments the DNA or RNA of an organism and reads the sequences using a machine. NGS technology involves extracting DNA, fragmenting the DNA into short sequence fragments, and performing sequencing to analyze the bases included in each sequence fragment. In one embodiment of the present invention, an Illumina sequencing method may be used as the NGS method, but it is not limited thereto.

(c) mapping genes from the gDNA isolated from the nail; and/or (d) calculating the expression levels of genes associated with the onset of a disease. The nail-based NGS analysis method of the present invention may further include the steps of:

The mapping step (c) refers to identifying the genomic location of each sequence fragment after sequencing the gDNA isolated from the nail by aligning each fragment based on a reference genome. In this context, a reference genome is a virtual nucleotide sequence of an individual representing a biological species, serving as a genomic map that guides the mapping process. After identifying the locations of all sequence fragments, various analyses can be performed, such as analyzing DNA mutations or measuring the amount of DNA transcribed into RNA.

The calculation step (d) refers to calculating the expression of genes associated with the onset of a disease by obtaining probabilities that the analysis subject data corresponds to each of the genotypes related to the analysis subject gene, based on the mapping results of step (c). Here, “analysis subject data” refers to genomic sequence data obtained through an individual's nail-based NGS (Next-Generation Sequencing) analysis. This data includes information necessary for identifying individual genetic characteristics, including genetic variations related to specific diseases or health conditions. Additionally, “analysis subject genes” are genes known to be associated with the onset of specific diseases; for example, they can be used to evaluate the impact on disease susceptibility by analyzing genotypes at specific SNP (Single Nucleotide Polymorphism) positions.

The calculation step (d) includes identifying positions and sequences with variations or detecting and identifying somatic or germline mutations based on the mapping results of step (c). The predicted results can be comparatively analyzed with results from Whole Genome Sequencing (WGS), which analyzes the entire genome; Whole Exome Sequencing (WES), which examines the protein-coding regions of all genes; or targeted sequencing, which analyzes only specific genetic regions.

In the present invention, genomic information extracted from nails is read using the Whole Exome Sequencing technique, where it is most crucial to detect a small number of reads (DNA fragments whose base sequences are read by sequencing equipment) without omission even at a low depth. Accordingly, the present inventors developed and applied a variant caller algorithm (Bullseye) that identifies low-frequency variations by reading information from all reads.

The calculation step (d) according to the present invention includes aligning and mapping the sequenced reads to a reference genome and then identifying positions and sequences with variations based on the information of the mapped reads using a variant caller algorithm. Since the variant caller algorithm (Bullseye) of the present invention can be used to detect and identify differences between the reference genome and the reads, specifically somatic or germline mutations, Bullseye utilizes a method with improved accuracy through comparative validation with the existing Mutect2, reducing errors of false positives or false negatives.

Additionally, the nail-based NGS analysis method of the present invention may further include a step of (e) constructing a genomic database using the calculation results of step (d).

The step (e) of constructing a genomic database may involve building an individual's genomic information catalog based on result reports for disease prevention and prediction by identifying individual germline and somatic mutations.

Furthermore, the present invention provides a method of providing information for the prevention, diagnosis, or prognostic diagnosis of diseases based on an individual's germline and somatic mutations.

The genomic information catalog of step (e) may include SNP-based health information, which is a characteristic individual genotype; information related to exercise, nutrition, pharmacogenomics, immunity, skin, mental health, digestion, metabolic hormones, and metabolism; and disease-related content including the occurrence, recurrence, and metastasis of chronic diseases and cancer.

Specifically, the genomic information catalog may include genomic databases such as SNV, INDEL, and CNV obtained through nail-derived gDNA-based WGS (whole-genome sequencing), WES (whole-exome sequencing), and targeted sequencing from samples of healthy individuals or patients, as well as basic clinical data databases of patients with chronic diseases or cancer.

(a1) loading a computer program executed by one or more processors; (b1) providing a memory that stores data in which genotypes for the genomic analysis subject are determined; (c1) generating an individual genetic analysis result report based on the stored data; and (d1) a system construction unit for building a genomic database of the analysis subject and a report generation step for providing information from the system construction unit to the analysis subject. The present invention relates to a system for predicting or diagnosing diseases using the aforementioned NGS analysis method, comprising the following steps:

In the step (a1), the one or more processors may perform: (i) an operation step of receiving a nail sample, general information, and/or clinical information of the analysis subject to acquire and store data; (ii) a gDNA sample analysis operation step of mapping the analysis subject data to each of the nucleotide sequences with different genotypes regarding the analysis subject genome by tracking disease-related genes such as the occurrence of chronic diseases and/or the occurrence, recurrence, or metastasis of cancer in the nail sample, preferably in gDNA isolated from the nail; and (iii) a clinical information analysis operation step of calculating the expression levels of genotypes in the analysis subject data based on the mapping results.

The general information in (i) refers to information provided based on individual consent, such as age, gender, family history, or lifestyle; the clinical information refers to information such as blood pressure, blood glucose, smoking, drinking, disease history, vaccine and immunization information, regular checkup items (blood pressure, blood glucose, cholesterol, cancer screening, etc.), types and doses of prescribed drugs, side effect history, or allergic reactions, which may be provided by primary to secondary hospitals.

The system for predicting or diagnosing diseases according to the present invention may further include a step of (e) constructing a big data database after the report generation step (d1). The report may include an individual's constitution, customized nutrition and diet, exercise, stress relief and psychological/mental health management, recommended activities, combinations of health functional foods, supplements, regular health checkup cycles and methods, or trackable health goals and progress.

The system for predicting or diagnosing diseases according to the present invention can also be applied as a veterinary genetic testing method for animals with nails, such as dogs and cats.

In the present invention, diseases may include, but are not limited to, aging, obesity, hypertension, diabetes, hyperlipidemia, cancer, sarcopenia, osteoporosis, lung disease, cardiovascular disease, brain disease, liver disease, dementia, or Alzheimer's disease.

By complementing the disadvantages of conventional tissue-based and blood-based liquid biopsies, the individual genome analysis derived from nails according to the present invention not only enables the identification and proactive response to disease risks and health abnormalities but also makes precision medicine for personalized treatment possible.

As the incidence of diseases including obesity, aging, hyperlipidemia, diabetes, or cancer is increasing due to the interaction between genetic markers and environmental factors, disease prevention has become a matter of critical importance. Diagnostic technology using specimens that can be easily collected, stored, and transported in daily life, such as nail (fingernail or toenail) specimens, enables active health management to prevent and improve diseases in humans and animals. This can ultimately realize an efficient QOL (Quality of Life) by extending a healthy lifespan.

Hereinafter, embodiments and examples of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art to which the present invention pertains can easily implement them. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments and examples described herein.

Throughout the specification of the present disclosure, when a certain part is described as “comprising” a certain component, this means that it may further include other components rather than excluding other components, unless otherwise stated.

Hereinafter, the present invention will be described in more detail through examples; however, the following examples are for illustrative purposes only and are not intended to limit the scope of the present invention.

Isolation of gDNA from Nail Samples

In the present invention, in order to minimize damage during nail DNA extraction, an extraction method was applied based on Forensic DNA Kits (OMEGA BIO-TEK E.Z.N.A.®, GeneAll Biotechnology Exgene™ Forensic SV mini, etc.) produced by domestic and international manufacturers, implementing a phenol/chloroform-free approach using approximately 10~20 mg of a single nail as a standard; and the volumes of the reaction solution and the reaction time for DNA elution were partially adjusted and applied to be suitable for nail specimens.

Sample QC was performed by verifying DNA concentration, purity, and fragment length; and the quality evaluation and analysis results for the genomic DNA (gDNA) samples were confirmed according to the TapeStation gDNA Screen Tape method, which is one of the microelectrophoresis analysis techniques.

Sample quality evaluation using the TapeStation is a method to quickly check the quality of gDNA samples, whereby it can be confirmed whether the samples are in a clean and intact state, and problems due to contamination or degradation can be identified. Additionally, since it is used to measure the concentration of gDNA, the amount of DNA included in the sample can be quantitatively calculated. Furthermore, it can visualize and analyze the size distribution of gDNA, making it possible to check the amount of DNA fragments in a specific size range or detect unusual phenomena in the size distribution; thus, it is suitable as a QC method for evaluating the quality of extracted DNA.

2 FIG. The samples used for comparative analysis were nail-derived gDNA from the same individual who was determined to be in the prediabetes and hypertension risk group through a general health checkup, and as control groups for comparison, (whole) blood-derived gDNA, plasma-derived cfDNA, and saliva-derived gDNA, which are currently mainly used in liquid biopsy. In addition, by comparing and analyzing with a nail-derived gDNA sample from a maternal donor who is a patient diagnosed with diabetes, DNA extraction results suitable for NGS analysis for the prevention and prediction of diabetes were derived through the observation of actual nail-derived germline mutations and somatic mutations, and the results are shown in Table 1 andbelow.

TABLE 1 Concen- Concen- Library tration tration Size Library name Type (ng/ul) (nM) (bp) Result 1 Nail SureSelect 29.8 103.5 443 Pass (Prediabetes and V6- Post hypertension (gDNA) risk group) 2 Whole blood SureSelect 43.6 177.9 377 Pass (Prediabetes and V6- Post hypertension (gDNA) risk group) 3 Saliva SureSelect 36 143.7 385 Pass (Prediabetes and V6- Post hypertension (gDNA) risk group) 4 Plasma SureSelect 25.7 122.9 322 Pass (Prediabetes and V6- Post hypertension (cfDNA) risk group) 5 Nail SureSelect 28 108 399 Pass (Diabetes V6- Post patient group) (gDNA)

Whole-exome sequencing (WES) is a method of analyzing only the sequences of the exome region, excluding non-coding regions; it can be seen as an example of Target Sequencing in that it targets specific regions; its primary purpose is to capture and amplify target regions and then search for specific variants through comparison with other samples. Compared to Whole Genome Sequencing (WGS), which analyzes the entire genome sequence, WES is easier to analyze due to its smaller data volume; furthermore, since most variants known to be associated with diseases occur in exon regions, WES analysis was conducted using NGS-based sequencing at a depth of 200× to 300×, which is an effective method in terms of time and analysis costs.

Compared to panel-based NGS analysis, which typically targets 10~120 types of gene mutations at a depth of 1,000×~10,000× or more for cancer diagnosis, performing sequencing at a lower depth of 200×~300× may lead to lower selection accuracy for variant genes, potentially making it insufficient for use in diagnostic fields. Therefore, while utilizing WES analysis capable of identifying more than 20,000 variants, an important algorithm used for identifying mutations (variants) by analyzing genomic data was developed and utilized for analysis to ensure improved variant detection performance in terms of sensitivity and accuracy aiming to reduce false positives and achieve 100% detection of missed variants. Analysis results were derived by comparing and double-verifying this with the Mutect2 variant caller, which is widely used by the majority of relevant researchers and companies domestically and abroad.

A package for receiving input arguments. A. import argparse A package required for calculating depth during the vcf creation stage. B. import numpy as np A package for storing and processing variant information as DataFrames. C. import pandas as pd A package required for loading and writing dictionaries (used for result filtering). D. import pickle A package used for loading read information from bam files. E. import pysam A package used for encrypting variant patterns. F. import random A package for manipulating strings (used to extract variant information from cigarstrings and md tags). G. import re A package for scientific computing (used for filtering strand bias). H. import scipy.stats as stats 1. Loading Python Packages Loads the positional information and sequences of reads contained in the md tags, cigarstrings, and bamfiles, and the corresponding reference genome. Extracts positional information where snvs and deletions occurred using the re package.—cnt(list) Identifies the specific types of bases where snvs and deletions occurred using the re package.—seq(list) Trims soft clips from the read sequences and modifies the cigarstrings accordingly.—cigar(string) (1) A soft clip indicates a portion (primarily at both ends) that does not match when a read is aligned to the reference genome, mostly corresponding to adapter or molecular barcode sequences. Stores the read sequence as a variable.—read_seq(string) (1) Categorizes cases where soft clips exist on the left, right, or both sides, and preserves only the purely mapped read sequence based on the deletion positions. Loads the reference genome sequence at the position where the read is mapped.—ref_seq(string) Stores the genomic position of the read as a variable.—block_start(int) A. read_info A preparation process for detecting indels; uses the cigar. Identifies patterns of M (match), D (deletion), and I (insertion) from the cigar.—indel_pattern(list) Identifies positional information for M (match), D (deletion), and I (insertion) from the cigar.—indel_iter(list) For example, if the cigar is 52M2I28M2D63M, it indicates that 2 insertions occur after 52 base matches, 2 bases are deleted after 28 matches, and the remaining 63 bases match. In this case, indel_pattern becomes [′M′, ‘I’, ‘M’, ‘D’, ‘M’] and indel_iter becomes [52, 2, 28, 2, 63]. Using a for loop, the relative positions of deletions and insertions within the read and the number of inserted/deleted bases are identified by moving the positions of the read and the reference along the indel patterns. del_pos_list(list), ins_pos_list(list), del_size(list), ins_size(list) An explanation of the above example is as follows: (1) Since the first indel element of the indel_pattern is I (insertion), ref_pos is added by the matching number (52) before the insertion. Since it is the first indel, alt_pos is also the same. ins_pos_list is based on alt_pos. The insertion position obtained in this way is added to ins_pos_list. That is, ref_pos: 52, alt_pos: 52, ins_pos_list: [52]. (2) The second indel element of the indel_pattern is D (deletion), and ref_pos is added by the matching number (28) after the existing ref_pos (Step 1:52). At this time, for alt_pos (Step 1:52), since two bases were inserted in the previous Step 1, the inserted 2 is added to the existing alt_pos, and then the matching number (28) is added. del_pos_list is based on ref_pos. That is, ref_pos: 80, alt_pos: 82, del_pos_list: [80]. (3) Summarizing the above results, B. prep_indel 2. Functions The developed variant caller algorithm is established through the following process, and the filtering step was improved based on a linear regression model obtained from simulation data to ensure high accuracy; in particular, it is a method optimized to maintain high sensitivity even in samples with low variant allele frequency.

Using del_pos_list/del_size and ins_pos_list/ins_size obtained from the prep_indel process, the genomic position of the indel and the deleted or inserted base sequences are stored as a dictionary variable.—key: variant type (indel), value: genomic position, ref, alt (list) (1) Insertion: As inserted bases, the base information can be found in read_seq. The inserted base sequence is obtained using the insertion position and insertion size in read_seq. (2) Deletion: As deleted bases, the base information can be found in ref_seq. The deleted base sequence is obtained using the deletion position and deletion size in ref_seq. C. call_indel As a function to detect snvs, the cnt and seq lists obtained from the md tag are used. Since the position information of snvs varies depending on the presence of indels, the output variables of call_indel are used as input variables. Similar to prep_indel, the read and reference sequences are moved from left to right to find the position corresponding to cnt.—Using a for loop. 0 is designated as the initial value for ref_pos (reference) and alt_pos (read). (Initial starting point) From cnt=[34] and seq=[‘C’], it can be identified that the 34th C base was substituted with another base and that the total number of snvs is one. Moving 34 bases from the left in ref_seq results in C, and moving 34 bases in read_seq results in G. That is, the 35th C has been changed to G. If one of the cnt elements starts with {circumflex over ( )}, it means a deletion occurred and it is not an snv; therefore, only ref_pos increases (because it was deleted) and alt_pos does not change. Conversely, if an insertion occurs, the information is not in cnt but in ins_pos_list (referred to as alt_pos_list in this function), which is an output of prep_indel; in this case, ref_pos does not change and only alt_pos increases. alt_pos retrieves position information from cnt, but since cnt does not contain insertion information, calculations must consider the relative positions of the substitution and insertion of the corresponding cnt to prevent duplicate insertion information when calculating with reference to ins_pos_list. That is, alt_pos increases by the number of inserted bases only when the insertion appears before the substitution. At this time, the insertion position is stored in a variable (last_ins) to prevent alt_pos from increasing redundantly. The information obtained from this is stored in the var_dict dictionary, similar to call_indel. key: variant type (snv), value: genomic position, ref, alt (list) D. call_snv The process of converting the variant information stored in the dictionary into lists by dividing them into forward read/reverse reads is performed. Each element of this list contains a single line string, which sequentially represents chromosome, genomic_position, ref, and alt. After converting this list into a dataframe using pandas, the identical variants are counted. The depth of each variant position is calculated using the count function of pysam. Two dataframes, which store the variants of the forward/reverse reads respectively, are produced as the final output. E. make_res It is a process of filtering each variant based on its own criteria. low_vaf (1) Create a fastq without variant information using art_illumina, a fastq simulator. All variants generated at this time are treated as errors. A linear regression model is created using the maximum value of the error count for each depth. (2) If a variant from make_res is lower than the value of this model, it falls into the error category and will be tagged with a low_vaf flag in the subsequent make_vcf process. low_depth (1) The average coverage of whole exome sequencing is 50, and variants with a depth of 40 or less will be tagged with this flag. This value is subject to change depending on experimental results. strand_bias (1) Strand bias refers to a phenomenon where variants are concentrated in either forward or reverse reads. (2) The Broad Institute recommends calculating the FisherStrand probability, and the same criteria were applied to this algorithm. (3) By retrieving depth and count information from the final dataframe, [[depth1, depth2], [read1, read2]] is stored in a variable. (4) The p-value is obtained using the scipy.stats package. (5) The Phred score is obtained. (6) If the Phred score is less than 60, it is determined that there is no strand bias. Germline/panel of normal filter (1) The panel of normal vcf (variants appearing in normal groups) and germline variants provided by the Broad Institute are excluded from the results. F. filter_output In the process of detecting variants, if multiple variants are found in a single read, they are stored separately as outputs. Using getrandbits from the random package, a unique ID (a 32-bit value) is assigned to each variant. Identical variants have the same ID. The ID assigned to each variant is stored in a dictionary named patterninfo. If three variants exist in a single read, they are stored in a pattern (list) in the form of [ID1, ID2, ID3]. Once variant detection is complete, identical patterns within the pattern are converted into a dataframe and counted. The variant information matching the IDs is retrieved from patterninfo. G. find_pattern The process of creating a VCF from the result dataframe. After loading a template VCF, information is retrieved from the dataframe to fill each column. It consists of ‘#CHROM’, ‘POS’, ‘ID’, ‘REF’, ‘ALT’, ‘QUAL’, ‘FILTER’, ‘INFO’, ‘FORMAT’, and the sample name. H. make_vcf

7 FIG. To compare the in-house caller (Bullseye) developed in the method described above with a widely used Variant caller (Mutect2), variants were detected from pre-processed bam files using the (in-house caller) to obtain vcf files; subsequently, annotation was performed on the VcF files using ensembl's vep, and the Vep outputs were integrated and processed with the vcf information. When analysis results were derived by filtering based on specific disease-related genes, in the case of WES gene variant analysis related to sarcopenia, it was confirmed that approximately 40 times more disease-related variants were additionally detected when applying the developed in house caller algorithm compared to the existing variant caller (Mutect2), as shown in.

8 FIG. In addition, as a result of comparative analysis using a non-small cell lung cancer reference material (Seracare Tri-Level Tumor Mutation DNA Mix v2), as shown in Table 2 andbelow, the in house caller detected all variants included in the reference material, whereas Mutect2 failed to detect two of the variants.

TABLE 2 Legacy Genomic AA CDS Genomic ID mutation ID Gene mutation mutation coordinates Mutect2 Bullseye COSM476 COSV56056643 BRAF p.V600E c.1799T > A chr7:140753336 o o COSM12378 COSV51769298 EGFR p.D770_N771insG c.2310_2311insGGT chr7:55181319- o o 55181320 COSM6225 COSV51765066 EGFR p.E746_A750del c.2236_2250del chr7:55174773- x o 55174787 COSM6240 COSV51765492 EGFR p.T790M c.2369C > T chr7:55181378 x o COSM6224 COSV51765161 EGFR p.L858R c.2573T > G chr7:55191822 o o COSM521 COSV55497369 KRAS p.G12D c.35G > A chr12:25245350 o o COSM6530 COSV52760886 TP53 p.C242Afs*5 c.723del chr17:7674241 o o COSM10662 COSV52661580 TP53 p.R248Q c.743G > A chr17:7674220 o o COSM10648 COSV52661038 TP53 p.R175H c.524G > A chr17:7675088 o o COSM10660 COSV52660980 TP53 p.R273H c.818G > A chr17:7673802 o o

Cases of false negatives have been reported several times in Mutect2, which is most representatively used and known for its good performance at low frequencies (Krøigård, A. B., Thomassen, M., Lænkholm, A.-V., Kruse, T. A., & Larsen, M. J. (2016). Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and targeted Deep Sequencing Data. PLOS ONE, 11(3), e0151664). That is, there are cases where clinically significant variants are not detected, and there may be a possibility of errors in the method of calculating the probability of a variant's presence using an internal probabilistic model of the detection algorithm.

The developed in house caller (Bullseye), when clinical samples and reference materials were comprehensively tested, was able to find variants even at low depth and frequency by using a method of identifying parts different from the reference genome sequence by reading all read information; as a result of the performance evaluation, it demonstrated sensitivity and accuracy 1.3 to 39.7 times or more superior to Mutect2.

Genetic Analysis Test for Patients with Chronic Diseases and Cancer

3 FIG. Genetic analysis tests based on nail specimens were conducted primarily for patients with chronic diseases and cancer. For primary verification, comparative analysis results of hereditary and non-hereditary disease markers related to diabetes were derived. In the observation results of genetic mutations related to diabetes, hypertension, and hyperlipidemia based on the germline mutations of a sample donor with borderline diabetes and hypertension, when comparing the detection from nail, plasma, and saliva samples to confirm the concordance rate of mutations detected against whole blood as a reference, it was confirmed that the nail samples exhibited similar or the highest detection patterns, as shown in. The above results were calculated as a percentage value by dividing the number of mutations commonly detected in each sample and whole blood by the total number of detected mutations.

4 FIG. In the observation results of genetic mutations related to diabetes, hypertension, and hyperlipidemia based on the somatic mutations of a sample donor with borderline diabetes and hypertension, when comparing the detection from nail, plasma, and saliva samples against whole blood as a reference, it was confirmed that the nail samples exhibited similar or the highest detection patterns, as shown in. The above results were calculated as a percentage value by dividing the number of mutations commonly found in each sample and whole blood by the total number of detected mutations.

In the case of the diabetes patient group, when filtering only for diabetes using an annotation tool, mutations were identified in genes, such as ALAD, ANKRD17, ASH1L, ATXN3, AZIN1, CACNA1E, CASQ2, CDC25A, CEL, CEP104, CPZ, CRB1, CTNS, DMXL2, EPC1, ERBB3, FAM193A, FAM20C, GALC, INPP4B, IVD, MAF, MAP3K9, MBTD1, MECOM, MED13, MYH3, NCOA2, NRXN1, PACSIN1, PDE6B, PIAS2, PPP3CB, PRKRA, PUM2, QKI, ROCK1, RPL7, SPAG9, TFDP2, TNRC6A, TTK, VPS13A, YTHDF3, ZC3H15, ZNF326 and ZNRF3, which are reported to be associated with existing diabetes having pathogenicity, confirming the possibility of predicting and diagnosing diabetes even in gDNA from nails (Table 3). Common genetic mutations in metabolic diseases, such as obesity, hyperlipidemia (HL), type 2 diabetes mellitus (T2DM), and metabolic syndrome (MS), were detected, and the groups indicated in gray belong to risk prediction markers based on type 1 diabetes mellitus (T1DM).

TABLE 3 ExonicFunc. #Chr Start End Ref Alt Ref.Gene Func.refGene refGene avsnp147 1 169000000 169000000 C T SLC19A2 UTR3 . rs2678166 1 169000000 169000000 C A SLC19A2 intronic . rs3765227 1 169000000 169000000 A G SLC19A2 intronic . rs1983546 1 169000000 169000000 G A SLC19A2 UTR5 . rs2072757 1 169000000 169000000 G C SLC19A2 upstream . rs2056926 2 10048522 10048522 A T KLF11 exonic synonymous rs11687357 SNV 2 10052724 10052724 — TT KLF11 UTR3 . rs34206941 2 88575373 88575373 C A EIF2AK3 exonic nonsynonymous rs1805165 SNV 2 88579613 88579613 T C EIF2AK3 exonic synonymous rs1805164 SNV 2 88583424 88583424 T A EIF2AK3 intronic . rs67509198 2 88585890 88585890 A G EIF2AK3 exonic nonsynonymous rs747376208 SNV 2 88595605 88595605 T C EIF2AK3 exonic nonsynonymous rs13045 SNV 2 88595833 88595833 T C EIF2AK3 intronic . rs7571971 2 88613755 88613755 G C EIF2AK3 exonic nonsynonymous rs867529 SNV 2 88626830 88626830 G C EIF2AK3 intronic . rs11680549 2 88627212 88627214 CAG — EIF2AK3 exonic nonframeshift rs144052332 deletion 2 171000000 171000000 C T DCAF17 exonic synonymous rs6751956 SNV 2 171000000 171000000 T A DCAF17 exonic nonsynonymous rs3731984 SNV 2 171000000 171000000 A T DCAF17 intronic . rs16859390 2 171000000 171000000 T C DCAF17 intronic . rs6741663 2 171000000 171000000 G T DCAF17 intronic . rs75082854 2 182000000 182000000 T C NEUROD1 exonic nonsynonymous rs1801262 SNV 3 57230844 57230844 T C APPL1 intronic . rs6789847 3 57236045 57236045 T — APPL1 intronic . rs397989738 3 57242231 57242231 A — APPL1 intronic . rs11346575 3 57253922 57253922 G A APPL1 intronic . rs3942261 3 57259269 57259269 T C APPL1 intronic . rs925566 3 57260057 57260057 C G APPL1 intronic . rs10510791 3 57260857 57260861 AAATT — APPL1 intronic . rs150865255 7 44145448 44145448 C T GCK intronic . rs13306387 7 44146057 44146057 G T GCK UTR5 . rs887688 7 44146188 44146188 C — GCK intronic . rs397890625 7 44146206 44146206 C G GCK intronic . rs887687 7 44146289 44146289 C A GCK intronic . rs887686 7 44147465 44147465 C G GCK intronic . . 7 44147948 44147949 TG — GCK intronic . . 7 44147950 44147950 G A GCK intronic . . 7 44149722 44149722 A G GCK intronic . rs2268574 7 44150869 44150869 T G GCK intronic . rs2268573 7 44152604 44152604 A G GCK intronic . rs12534623 7 44157984 44157984 G T GCK intronic . rs2070971 7 44159345 44159345 — C GCK intronic . rs397691101 7 44189037 44189037 G C GCK UTR5 . rs13306391 7 128000000 128000000 T C PAX4 exonic stoploss rs712700 7 128000000 128000000 T G PAX4 exonic nonsynonymous rs712701 SNV 7 128000000 128000000 A C PAX4 intronic . rs806192 7 128000000 128000000 GGAT — PAX4 intronic . rs376039555 7 128000000 128000000 G A PAX4 exonic nonsynonymous rs114315130 SNV 7 128000000 128000000 — G PAX4 intronic . rs3835004 7 128000000 128000000 G A PAX4 intronic . rs594141 7 128000000 128000000 C G PAX4 intronic . rs698406 7 128000000 128000000 C G PAX4 intronic . rs327516 8 11543119 11543119 C G BLK intronic . rs12386974 8 11546260 11546260 C G BLK intronic . rs2244931 8 11547832 11547832 C A BLK intronic . rs2306232 8 11547928 11547928 G A BLK intronic . rs6994605 8 11549306 11549306 T C BLK intronic . rs4841553 8 11553004 11553004 C A BLK intronic . rs13254325 8 11553029 11553029 C T BLK intronic . rs2255227 8 11556504 11556504 G C BLK intronic . rs11250147 8 11556728 11556728 T C BLK exonic synonymous rs2306234 SNV 8 11558613 11558613 T C BLK intronic . rs2467518 8 11559984 11559984 T C BLK intronic . rs10098664 8 11560073 11560073 T C BLK intronic . rs10098782 8 11560876 11560876 G A BLK intronic . rs2898289 8 11561264 11561264 C T BLK intronic . rs4841561 8 11563812 11563812 T C BLK intronic . rs13248757 8 11563849 11563849 C A BLK intronic . rs10097005 8 11563875 11563875 C T BLK intronic . rs10097015 8 11564284 11564284 C T BLK UTR3 . rs1042689 8 11564780 11564780 G C BLK downstream . rs2250986 9 133000000 133000000 G A CEL intronic . rs8193019 9 133000000 133000000 C T CEL exonic nonsynonymous rs199524286 SNV 9 133000000 133000000 T — CEL exonic frameshift rs778607687 deletion 9 137000000 133000000 A G AGPAT2 intronic . rs9411215 9 137000000 137000000 C T AGPAT2 intronic . rs188750172 9 137000000 137000000 C G AGPAT2 intronic . rs2236514 11 2159830 2159830 T G INS UTR3 . rs3842753 11 2160774 2160774 A G INS intronic . rs5506 11 2160994 2160994 A T INS UTR5 . rs689 11 17386857 17386857 C T KCNJ11 UTR3 . rs5213 11 17387083 17387083 C T KCNJ11 exonic nonsynonymous rs5215 SNV 11 17387522 17387522 G A KCNJ11 exonic synonymous rs5218 SNV 11 17388025 17388025 T C KCNJ11 exonic nonsynonymous rs5219 SNV 11 17388598 17388598 A — KCNJ11 UTR5 rs35980606 11 17388736 17388736 C T KCNJ11 intronic rs5222 12 121000000 121000000 C G HNF1A exonic synonymous rs1169289 SNV 12 121000000 121000000 A C HNF1A exonic nonsynonymous rs1169288 SNV 12 121000000 121000000 A G HNF1A intronic . rs2244608 12 121000000 121000000 C T HNF1A intronic . rs1169292 12 121000000 121000000 G A HNF1A intronic . rs1169293 12 121000000 121000000 G A HNF1A intronic . rs1169294 12 121000000 121000000 G A HNF1A intronic . rs1169300 12 121000000 121000000 T A HNF1A intronic . rs2071190 12 121000000 121000000 C T HNF1A intronic . rs1169301 12 121000000 121000000 T G HNF1A intronic . rs1169302 12 121000000 121000000 — TCATTCAT HNF1A intronic . rs397707647 12 121000000 121000000 G A HNF1A intronic . rs2259852 12 121000000 121000000 C T HNF1A exonic synonymous rs2259820 SNV 12 121000000 121000000 G A HNF1A exonic nonsynonymous rs2464196 SNV 12 121000000 121000000 G A HNF1A intronic . rs2464195 12 121000000 121000000 G T HNF1A intronic . rs2259816 12 121000000 121000000 T C HNF1A intronic . . 12 121000000 121000000 C T HNF1A intronic . rs56097722 12 121000000 121000000 T C HNF1A intronic . rs1169304 12 121000000 121000000 A G HNF1A exonic nonsynonymous rs1169305 SNV 12 121000000 121000000 G T HNF1A UTR3 . rs1169309 12 121000000 121000000 G A HNF1A UTR3 . rs1169310 12 121000000 121000000 G A HNF1A UTR3 . rs11065390 13 27920030 27920030 G — PDX1 UTR5 . rs146021107 13 27924128 27924128 C G PDX1 intronic . rs60353775 13 27924186 27924186 G C PDX1 intronic . rs9805632 13 27945173 27945173 G A PDX1 intergenic . rs7995110 13 27945217 27945217 A C PDX1 intergenic . rs2504220 13 95757861 95757861 C T DNAJC3 intronic . rs3087339 20 3082515 3082515 T C AVP downstream . rs1887854 20 3084303 3084303 C T AVP intronic . rs2282018 20 44356014 44356014 G C HNF4A intronic . . 20 44406020 44406020 T C HNF4A intronic . rs736824 20 44428840 44428840 G T HNF4A intronic . rs3818247 InterVar: clinvar: InterVar and #Chr AAChange.refGene Clinvar Evidence Associated_disease 1 . clinvar: InterVar: Benign Rogers Benign PVS1 = 0 PS = [0, 0, syndrome<br>TRM 0, 0, 0] PM = [0, 0, A<br>Thiamine- 0, 0, 0, 0, 0] responsive PP = [0, 0, 0, 0, 0, megaloblastic 0] BA1 = 1 BS = [1, anemia with 0, 0, 0, 0] BP = [0, diabetes mellitus 0, 0, 0, 0, 1, 0, 0] and sensorineural deafness 1 . clinvar: InterVar: Benign Rogers — Likely PVS1 = 0 PS = [0, 0, syndrome<br>TRM benign 0, 0, 0] PM = [0, 0, A<br>Thiamine- 0, 0, 0, 0, 0] responsive PP = [0, 0, 0, 0, 0, megaloblastic [0] BA1 = 1 BS = [1, anemia with 0, 0, 0, 0] BP = [0, diabetes mellitus 0, 0, 0, 0, 1, 0, 0] and sensorineural deafness 1 . clinvar: InterVar: Benign Rogers Benign PVS1 = 0 PS = [0, 0, syndrome<br>TRM 0, 0, 0] PM = [0, 0, A<br>Thiamine- 0, 0, 0, 0, 0] responsive PP = [0, 0, 0, 0, 0, megaloblastic 0] BA1 = 1 BS = [1, anemia with 0, 0, 0, 0] BP = [0, diabetes mellitus 0, 0, 0, 0, 1, 0, 0] and sensorineural deafness 1 . clinvar: InterVar: Benign Rogers Benign PVS1 = 0 PS = [0, 0, syndrome<br>TRM 0, 0, 0] PM = [0, 0, A<br>Thiamine- 0, 0, 0, 0, 0] responsive PP = [0, 0, 0, 0, 0, megaloblastic 0] BA1 = 1 BS = [1, anemia with 0, 0, 0, 0] BP = [0, diabetes mellitus 0, 0, 0, 0, 1, 0, 0] and sensorineural deafness 1 . clinvar: InterVar: Benign Rogers Benign PVS1 = 0 PS = [0, 0, syndrome<br>TRM 0, 0, 0] PM = [0, 0, A<br>Thiamine- 0, 0, 0, 0, 0] responsive PP = [0, 0, 0, 0, 0, megaloblastic 0] BA1 = 1 BS = [1, anemia with 0, 0, 0, 0] BP = [0, diabetes mellitus 0, 0, 0, 0, 1, 0, 0] and sensorineural deafness 2 KLF11: NM_001177716: clinvar: InterVar: Benign Maturity-onset exon3: c.A1134T: p.V378V, UNK PVS1 = 0 PS = [0, 0, diabetes of the KLF11: NM_001177718: 0, 0, 0] PM = [0, 0, young exon3: c.A1134T: pV378V, 0, 0, 0, 0, 0] KLF11: NM_003597: PP = [0, 0, 0, 0, 0, exon3: c.A1185T: pV395V 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 1, 0, 0, 1, 0] 2 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 2 EIF2AK3: NM_001313915: clinvar: InterVar: Benign Early-onset diabetes exon13: c.G1657T: p.A553S, Benign PVS1 = 0 PS = [0, 0, mellitus with EIF2AK3: NM_004836: 0, 0, 0] PM = [0, 0, multiple epiphyseal exon13: c.G2110T: p.A704S 0, 0, 0, 0, 0] dysplasia<br>WRS PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 2 EIF2AK3: NM_001313915: clinvar: InterVar: Benign Early-onset diabetes exon11: c.A1338G: p.Q446Q, Benign PVS1 = 0 PS = [0, 0, mellitus with EIF2AK3: NM_004836: 0, 0, 0] PM = [0, 0, multiple epiphyseal exon11: c.A1791G: p.Q597Q 0, 0, 0, 0, 0] dysplasia<br>WRS PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 1, 0, 1, 1, 0] 2 . clinvar: InterVar: Benign Early-onset diabetes Benign PVS1 = 0 PS = [0, 0, mellitus with 0, 0, 0] PM = [0, 0, multiple epiphyseal 0, 0, 0, 0, 0] dysplasia<br>WRS PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 2 EIF2AK3: NM_001313915: clinvar: InterVar: Uncertain Early-onset diabetes exon9: c.T1148C: p.I383T, — Uncertain significance mellitus with EIF2AK3: NM_004836: significance PVS1 = 0 PS = [0, 0, multiple epiphyseal exon9: c.T1601C: p.I534T 0, 0, 0] PM = [1, 1, dysplasia<br>WRS 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 0 BS = [0, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 2 EIF2AK3: NM_001313915: clinvar: InterVar: Benign Early-onset diabetes exon3: c.A44G: p.Q15R, Benign PVS1 = 0 PS = [0, 0, mellitus with EIF2AK3: NM_004836: 0, 0, 0] PM = [1, 0, multiple epiphyseal exon3: c.A497G: p.Q166R 0, 0, 0, 0, 0] dysplasia<br>WRS PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 2 . clinvar: InterVar: Benign Early-onset diabetes UNK PVS1 = 0 PS = [0, 0, mellitus with 0, 0, 0] PM = [0, 0, multiple epiphyseal 0, 0, 0, 0, 0] dysplasia<br>WRS PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 2 EIF2AK3: NM_004836: clinvar: InterVar: Benign Early-onset diabetes exon2: c.C407G: p.S136C Benign PVS1 = 0 PS = [0, 0, mellitus with 0, 0, 0] PM = [0, 0, multiple epiphyseal 0, 0, 0, 0, 0] dysplasia<br>WRS PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 2 . clinvar: InterVar: Benign Early-onset diabetes UNK PVS1 = 0 PS = [0, 0, mellitus with 0, 0, 0] PM = [0, 0, multiple epiphyseal 0, 0, 0, 0, 0] dysplasia<br>WRS PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 2 EIF2AK3: NM_04836: clinvar: InterVar: Benign Early-onset diabetes exon1: c61_63del: Benign PVS1 = 0 PS = [0, 0, mellitus with p.L21del 0, 0, 0] PM = [0, 0, multiple epiphyseal 0, 0, 0, 0, 0] dysplasia<br>WRS PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 1, 0, 0, 1, 0, 0] 2 DCAF17: NM_001164821: clinvar: InterVar: Benign Diabetes- exon2: c.C150T: p.V50V, Benign PVS1 = 0 PS = [0, 0, hypogonadism- DCAF17: NM_025000: 0, 0, 0] PM = [0, 0, deafness- exon2: c.C150T: p.V50V 0, 0, 0, 0, 0] intellectual PP = [0, 0, 0, 0, 0, disability syndrome 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 1, 0, 1, 1, 0] 2 DCAF17: NM_001164821: clinvar: InterVar: Likely Diabetes- exon6: c.T555A: p.H185Q, Benign benign PVS1 = 0 hypogonadism- DCAF17: NM_025000: PS = [0, 0, 0, 0, 0] deafness- exon6: c.T555A: p.H185Q PM = [1, 0, 0, 0, 0, intellectual 0, 0] PP = [0, 0, 0, disability syndrome 0, 0, 0] BA1 = 0 BS = [1, 0, 0, 0, 0] BP = [1, 0, 0, 1, 0, 1, 0, 0] 2 . clinvar: InterVar: Uncertain Diabetes- UNK significance hypogonadism- PVS1 = 0 PS = [0, 0, deafness- 0, 0, 0] PM = [0, 0, intellectual 0, 0, 0, 0, 0] disability syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 0 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 2 . clinvar: InterVar: Benign Diabetes- UNK PVS1 = 0 PS = [0, 0, hypogonadism- 0, 0, 0] PM = [0, 0, deafness- 0, 0, 0, 0, 0] intellectual PP = [0, 0, 0, 0, 0, disability syndrome 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 2 . clinvar: InterVar: Uncertain Diabetes- UNK significance hypogonadism- PVS1 = 0 PS = [0, 0, deafness- 0, 0, 0] PM = [0, 0, intellectual 0, 0, 0, 0, 0] disability syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 0 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 2 NEUROD1: NM_002500: clinvar: InterVar: Benign Maturity-onset exon2: c.A133G: p.T45A Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 3 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 3 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 3 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 3 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 3 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 3 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 3 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 . clinvar: InterVar: Benign Developmental Benign PVS1 = 0 PS = [0, 0, delay-epilepsy- 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 . clinvar: InterVar: Benign Developmental Benign PVS1 = 0 PS = [0, 0, delay-epilepsy- 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 . clinvar: InterVar: Benign Developmental Benign PVS1 = 0 PS = [0, 0, delay-epilepsy- 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 . clinvar: InterVar: Benign Developmental Benign PVS1 = 0 PS = [0, 0, delay-epilepsy- 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 . clinvar: InterVar: Benign Developmental UNK PVS1 = 0 PS = [0, 0, delay-epilepsy - 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 . clinvar: InterVar: Uncertain delay-epilepsy- UNK significance Developmental PVS1 = 0 PS = [0, 0, neonatal diabetes 0, 0, 0] PM = [0, 1, syndrome 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] 0] BA1 = 0 BS = [0, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 . clinvar: InterVar: Uncertain Developmental UNK significance delay-epilepsy- PVS1 = 0 PS = [0, 0, neonatal diabetes 0, 0, 0] PM = [0, 1, syndrome 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 0 BS = [0, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 . clinvar: InterVar: Uncertain Developmental UNK significance delay-epilepsy- PVS1 = 0 PS = [0, 0, neonatal diabetes 0, 0, 0] PM = [0, 1, syndrome 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 0 BS = [0, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 . clinvar: InterVar: Benign Developmental Benign PVS1 = 0 PS = [0, 0, delay-epilepsy- 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 . clinvar: InterVar: Benign Developmental Benign PVS1 = 0 PS = [0, 0, delay-epilepsy- 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 . clinvar: InterVar: Benign Developmental UNK PVS1 = 0 PS = [0, 0, delay-epilepsy- 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 . clinvar: InterVar: Benign Developmental Benign PVS1 = 0 PS = [0, 0, delay-epilepsy- 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 . clinvar: InterVar: Benign Developmental Benign PVS1 = 0 PS = [0, 0, delay-epilepsy- 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 . clinvar: InterVar: Benign delay-epilepsy- Benign PVS1 = 0 PS = [0, 0, Developmental 0, 0, 0] PM = [0, 0, neonatal diabetes 0, 0, 0, 0, 0] syndrome PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 PAX4: NM_001366111: clinvar: InterVar: Benign Maturity-onset exon10: c.A1046G: p.X349W Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 1, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 PAX4: NM_001366110: clinvar: InterVar: Benign Maturity-onset exon12: c.A986C: p.H329P Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [1, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 1, 0, 1, 0, 0] 7 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 PAX4: NM_001366111: clinvar: InterVar: Uncertain Maturity-onset exon6: c.C571T: p.R191C, — Likely significance diabetes of the PAX4: NM_001366110: benign PVS1 = 0 PS = [0, 0, young exon8: c.C571T: p.R191C 0, 0, 0] PM = [0, 0, 0, 0, 0, 0, 0] PP = [0, 0, 1, 0, 0, 0] BA1 = 0 BS = [0, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 7 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 7 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 BLK: NM_001330465: clinvar: InterVar: Benign Maturity-onset exon8: c.T630C: p.F210F, Benign PVS1 = 0 PS = [0, 0, diabetes of the BLK: NM_001715: 0, 0, 0] PM = [0, 0, young exon9: c.T843C: p.F281F 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 1, 0, 1, 1, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 8 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 9 . clinvar: InterVar: Uncertain Maturity-onset UNK significance diabetes of the PVS1 = 0 PS = [0, 0, young 0, 0, 0] PM = [0, 0, 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 0 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 9 CEL: NM_001807: clinvar: InterVar: Uncertain Maturity-onset exon10: c.C1412T: p.T471M UNK significance diabetes of the PVS1 = 0 PS = [0, 0, young 0, 0, 0] PM = [0, 0, 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 0 BS = [0, 0, 0, 0, 0] BP = [1, 0, 0, 0, 0, 0, 0, 0] 9 CEL: NM_001807: clinvar: InterVar: Likely Maturity-onset exon11: c.2205delT: UNK pathogenic diabetes of the p.V736Cfs*22 PVS1 = 1 PS = [0, 0, young 0, 0, 0] PM = [0, 1, 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 0 BS = [0, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 9 . clinvar: InterVar: Benign BSCL<br>Beradinelli- UNK PVS1 = 0 PS = [0, 0, Seip syndrome<br> 0, 0, 0] PM = [0, 0, Brunzell syndrome<br> 0, 0, 0, 0, 0] GCL<br>Generalized PP = [0, 0, 0, 0, 0, congenital 0] BA1 = 1 BS = [1, lipodystrophy<br> 0, 0, 0, 0] BP = [0, Lipoatrophic diabetes 0, 0, 0, 0, 0, 0, 0] 9 . clinvar: InterVar: Uncertain BSCL<br>Beradinelli- UNK significance Seip syndrome<br> PVS1 = 0 PS = [0, 0, Brunzell syndrome<br> 0, 0, 0] PM = [0, 0, GCL<br>Generalized 0, 0, 0, 0, 0] congenital PP = [0, 0, 0, 0, 0, lipodystrophy<br> 0] BA1 = 0 BS = [1, Lipoatrophic diabetes 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 9 . clinvar: InterVar: Benign BSCL<br>Beradinelli- UNK PVS1 = 0 PS = [0, 0, Seip syndrome<br> 0, 0, 0] PM = [0, 0, Brunzell syndrome<br> 0, 0, 0, 0, 0] GCL<br>Generalized PP = [0, 0, 0, 0, 0, congenital 0] BA1 = 1 BS = [1, lipodystrophy<br> 0, 0, 0, 0] BP = [0, Lipoatrophic diabetes 0, 0, 0, 0, 0, 0, 0] 11 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 1, 0, 1, 0, 0] 11 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 11 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 11 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 11 KCNJ11: NM_000525: clinvar: InterVar: Benign Maturity-onset exon1: c.G1009A: p.V337I, Benign/ PVS1 = 0 PS = [0, 0, diabetes of the KCNJ11: NM_001166290: — Likely 0, 0, 0] PM = [1, 0, young exon2: c.G748A: p.V250I, benign 0, 0, 0, 0, 0] KCNJ11: NM_001377297: PP = [0, 0, 0, 0, 0, exon2: c.G748A: p.V250I, 0] BA1 = 1 BS = [1, KCNJ11: NM_001377296: 0, 0, 0, 0] BP = [0, exon3: c.G748A: p.V250I 0, 0, 1, 0, 1, 0, 0] 11 KCNJ11: NM_000525: clinvar: InterVar: Benign Maturity-onset exon1: c.C570T: p.A190A, Benign/ PVS1 = 0 PS = [0, 0, diabetes of the KCNJ11: NM_001166290: — Likely 0, 0, 0] PM = [0, 0, young exon2: c.C309T: p.A103A, benign 0, 0, 0, 0, 0] KCNJ11: NM_001377297: PP = [0, 0, 0, 0, 0, exon2: c.C309T: p.A103A, 0] BA1 = 1 BS = [1, KCNJ11: NM_001377296: 0, 0, 0, 0] BP = [0, exon3: c.C309T: p.A103A 0, 0, 1, 0, 1, 1, 0] 11 KCNJ11: NM_000525: clinvar: InterVar: Benign Maturity-onset exon1: c.A67G: p.K23E — drug PVS1 = 0 PS = [0, 0, diabetes of the response 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 11 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 11 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 12 HNF1A: NM_000545: clinvar: InterVar: Benign Maturity-onset exon1: c.C51G: p.L17L, Benign PVS1 = 0 PS = [0, 0, diabetes of the HNF1A: NM_001306179: 0, 0, 0] PM = [0, 0, young exon1: c.C51G: p.L17L 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 1, 0, 1, 1, 0] 12 HNF1A: NM_000545: clinvar: InterVar: Benign Maturity-onset exon1: c.A79C: p.I27L, Benign PVS1 = 0 PS = [0, 0, diabetes of the HNF1A: NM_001306179: 0, 0, 0] PM = [0, 0, young exon1: c.A79C: p.I27L 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 HNF1A: NM_000545: clinvar: InterVar: Benign Maturity-onset exon7: c.C1375T: p.L459L, Benign PVS1 = 0 PS = [0, 0, diabetes of the HNF1A: NM_001306179: 0, 0, 0] PM = [0, 0, young exon7: c.C1375T: p.L459L 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 1, 0, 1, 1, 0] 12 HNF1A: NM_000545: clinvar: InterVar: Benign Maturity-onset exon7: c.G1460A: p.S487N, Benign PVS1 = 0 PS = [0, 0, diabetes of the HNF1A: NM_001306179: 0, 0, 0] PM = [0, 0, young exon7: c.G1460A: p.S487N 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 1, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Uncertain Maturity-onset UNK significance diabetes of the PVS1 = 0 PS = [0, 0, young 0, 0, 0] PM = [0, 1, 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 0 BS = [0, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset — not PVS1 = 0 PS = [0, 0, diabetes of the provided 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 1, 0, 0, 0, 0] 12 HNF1A: NM_000545: clinvar: InterVar: Benign Maturity-onset exon9: c.A1720G: p.S574G, Benign PVS1 = 0 PS = [0, 0, diabetes of the HNF1A: NM_001306179: 0, 0, 0] PM = [0, 0, young exon9: c.A1741G: p.S581G 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 12 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 13 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 13 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 13 . clinvar: InterVar: Benign Maturity-onset Benign PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 1, 0, 0] 13 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0, 0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 13 . clinvar: InterVar: Benign Maturity-onset UNK PVS1 = 0 PS = [0, 0, diabetes of the 0,0, 0] PM = [0, 0, young 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 13 . clinvar: InterVar: Benign Combined UNK PVS1 = 0 PS = [0, 0, cerebellar and 0, 0, 0] PM = [0, 0, peripheral ataxia- 0, 0, 0, 0, 0] hearing loss- PP = [0, 0, 0, 0, 0, diabetes mellitus 0] BA1 = 1 BS = [1, syndrome 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 20 . clinvar: InterVar: Benign CDI<br>Neurogenic UNK PVS1 = 0 PS = [0, 0, diabetes insipidus 0, 0, 0] PM = [0, 0, 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 20 . clinvar: InterVar: Benign CDI<br>Neurogenic UNK PVS1 = 0 PS = [0, 0, diabetes insipidus 0, 0, 0] PM = [0, 0, 0, 0, 0, 0, 0] PP = [0, 0, 0, 0, 0, 0] BA1 = 1 BS = [1, 0, 0, 0, 0] BP = [0, 0, 0, 0, 0, 0, 0, 0] 20 . clinvar: InterVar: Uncertain HNF1B- UNK significance MODY<br>MODY PVS1 = 0 PS = [0, 0, 5<br>RCAD 0, 0, 0] PM = [0, 1, syndrome<br>Renal 0, 0, 0, 0, 0] cysts-maturity- PP = [0, 0, 0, 0, 0, onset diabetes of the 0] BA1 = 0 BS = [0, young syndrome<br> 0, 0, 0, 0] BP = [0, Renal dysfunction-early- 0, 0, 0, 0, 0, 0, 0] onset diabetes syndrome 20 . clinvar: InterVar: Benign HNF1B- Benign PVS1 = 0 PS = [0, 0, MODY<br>MODY 0, 0, 0] PM = [0, 0, 5<br>RCAD 0, 0, 0, 0, 0] syndrome<br>Renal PP = [0, 0, 0, 0, 0, cysts-maturity- 0] BA1 = 1 BS = [1, onset diabetes of the 0, 0, 0, 0] BP = [0, young syndrome<br> 0, 0, 0, 0, 1, 0, 0] Renal dysfunction-early- onset diabetes syndrome 20 . clinvar: InterVar: Benign HNF1B- UNK PVS1 = 0 PS = [0, 0, MODY<br>MODY 0, 0, 0] PM = [0, 0, 5<br>RCAD 0, 0, 0, 0, 0] syndrome<br>Renal PP = [0, 0, 0, 0, 0, cysts-maturity- 0] BA1 = 1 BS = [1, onset diabetes of the 0, 0, 0, 0] BP = [0, young syndrome<br> 0, 0, 0, 0, 0, 0, 0] Renal dysfunction-early- onset diabetes syndrome

5 FIG. 6 FIG. Additionally, when identifying markers related to pancreatic cancer, which is one of the most frequently occurring carcinomas in diabetes patients, the mutation status of pancreatic cancer-related genes indicated in gray could be confirmed as shown in Table 4; although there were no mutations corresponding to Tier 1 or Tier 2, which are mutation criteria related to disease diagnosis and treatment decisions, numerous cases corresponding to Tier 3 mutations with unknown clinical significance that are of prophylactic interest for the future were identified (), and it was confirmed that these occurred more than 10 times more frequently compared to the borderline diabetes sample group inand Table 5.

TABLE 4 — PROTEIN SYMBOL PROTEIN_CHANGE GENE_NAME DOMAIN CONSEQUENCE 1 SPRED1 p.Ser37Gly sprouty related WH1 domain — missense EVH1 domain variant containing 1 2 SPRED1 p.Ser41Cys sprouty related WH1 domain — missense EVH1 domain variant containing 1 3 PALB2 p.Tyr743Cys partner and — missense localizer of variant BRCA2 5 STAT3 p.Thr663Ser signal transducer SH2 domain — missense and activator of variant transcription 3 6 CYLD p.Asp32Glu CYLD lysine 63 — missense deubiquitinase variant 7 CYLD — p.Leu567 CYLD lysine 63 — missense Ser568delinsPheCys deubiquitinase variant 8 FGFR2 p.Ser354Thr fibroblast growth Immunoglobulin — missense factor receptor 2 I-set domain variant 10 NOTCH1 p.His1735Gln notch receptor 1 — missense variant 11 NOTCH1 p.Thr1351Ala notch receptor 1 — missense variant 12 KMT2C p.Gly838Ser lysine — missense methyltransferase variant 2C 13 KMT2D p.Gln3839ArgfsTer41 lysine — frameshift methyltransferase variant 2D 14 CUX1 p.Ser637Asn cut like homeobox — missense 1 variant 15 SMAD3 p.Leu153Met SMAD family — missense member 3 variant 16 ATRX p.Glu929Gln ATRX chromatin — missense remodeler variant 17 ARHGAP26 p.Val699Ile Rho GTPase — missense activating protein variant 26 18 KDM6A p.Val156Ala lysine demethylase Tetratricopeptide — missense 6A repeat variant 19 KDM6A p.Arg664Gln lysine demethylase — missense 6A variant 20 KDM6A p.Thr668Ser lysine demethylase — missense 6A variant 21 TRIM33 p.Asp1050Glu tripartite motif — missense containing 33 variant 22 NDRG1 p.Gln127Lys N-myc downstream Ndr family — missense regulated 1 variant 23 NDRG1 p.Val125Ile N-myc downstream Ndr family — missense regulated 1 variant 24 QKI p.Ala225del QKI, KH domain — inframe containing RNA deletion binding 25 DLC1 p.Val1486Ile DLC1 Rho GTPase START — missense activating protein domain variant 26 KAT6B p.Met363Val lysine — missense acetyltransferase variant 6B 27 KDM5C p.Val1492Ala lysine demethylase — missense 5C variant 28 PATZ1 p.Leu533Pro POZ/BTB and AT — missense hook containing variant zinc finger 1 29 PATZ1 p.Pro531Ser POZ/BTB and AT — missense hook containing variant zinc finger 1 30 DMBT1 p.Arg670Cys deleted in Scavenger — missense malignant brain receptor variant tumors 1 cysteine-rich domain 31 S100A4 p.Asn68Ser S100 calcium — missense binding protein A4 variant 32 TGFBR3 p.Gly765Arg transforming — missense growth factor beta variant receptor 3 33 CIR1 p.Arg183Gln corepressor — missense interacting with variant RBPJ, CIR1 34 RBMS3 p.Pro437Gln RNA binding motif — missense single stranded variant, interacting protein — splice_region 3 variant 35 SHISA3 p.Phe235Cys shisa family — missense member 3 variant 36 TGFBI p.Arg257Trp transforming Fasciclin — missense growth factor beta domain variant, induced — splice_region variant 37 AZGP1 p.Pro20Leu alpha-2- — missense glycoprotein 1, variant zinc-binding 38 CELF2 CUGBP Elav-like — frameshift family member 2 variant, — splice_region variant 39 VWA5A p.Ile582Thr von Willebrand — missense factor A domain variant containing 5A 40 SUSD2 p.Arg34His sushi domain Somatomedin — missense containing 2 B domain variant 41 SUSD2 p.Gly70Val sushi domain — missense containing 2 variant 42 CUL4A p.Phe394Tyr cullin 4A Cullin family — missense variant 43 LYN p.Phe165LeufsTer42 LYN proto- SH2 domain — frameshift oncogene, Src variant family tyrosine kinase 44 ROS1 p.Cys1117Tyr ROS proto- — missense oncogene 1, variant receptor tyrosine kinase 45 ERBB3 p.Arg1202Trp erb-b2 receptor — missense tyrosine kinase 3 variant 46 NSD2 p.Pro234His nuclear receptor PWWP domain — missense binding SET variant domain protein 2 47 TLX1 p.Leu276Met T cell leukemia — missense homeobox 1 variant 48 ETV4 p.Val448Ile ETS variant — missense transcription factor variant 4 49 NFATC2 p.Pro237Ser nuclear factor of — missense activated T cells 2 variant 50 HGF p.Gln173Arg hepatocyte growth Kringle — missense factor domain variant 51 PBK p.Ser57Phe PDZ binding Protein kinase — missense kinase domain variant 52 RAB1A p.Gly201Ser RAB1A, member — missense RAS oncogene variant family 53 NNT p.Leu822PhefsTer8 nicotinamide NAD(P) — frameshift nucleotide transhydrogenase variant transhydrogenase beta subunit 54 NNT p.Thr823Ile nicotinamide NAD(P) — missense nucleotide transhydrogenase variant transhydrogenase beta subunit 55 RUNX2 p.Thr9Ser RUNX family — missense transcription factor variant 2 56 BRF2 p.Pro351Leu BRF2 RNA — missense polymerase III variant transcription initiation factor subunit

TABLE 5 — PROTEIN SYMBOL PROTEIN_CHANGE GENE_NAME DOMAIN CONSEQUENCE 1 SMAD3 p.Leu153Met SMAD family missense_variant member 3 2 ATRX p.Glu929Gln ATRX missense_variant chromatin remodeler 3 PAX6 p.Ter437GluextTer14 paired box 6 stop_lost 4 DMBT1 p.Arg670Cys deleted in Scavenger missense_variant malignant brain receptor cysteine- tumors 1 rich domain 5 DMBT1 p.Pro794Leu deleted in Scavenger missense_variant malignant brain receptor cysteine- tumors 1 rich domain 6 PTPRG p.Gly1146Arg protein tyrosine missense_variant phosphatase receptor type G 7 SHISA3 p.Phe235Cys shisa family missense_variant member 3 8 TGFBI p.Arg257Trp transforming Fasciclin domain missense_variant, growth factor — splice_region beta induced variant 9 SUSD2 p.Arg34His sushi domain Somatomedin B missense_variant containing 2 domain 10 GOLM1 p.Pro265His golgi missense_variant membrane protein 1

Based on the above results, the Tier 3 stage is a part that can be observed as disease recurrence and long-term outcomes of treatment in relation to health sustainability; therefore, it can be said that these analysis results can positively contribute to clients and patients by allowing them to predict the changing status of the disease through periodic monitoring of said part and to prepare preventive and management measures accordingly.

Furthermore, specifically, by enabling the observation of types of genetic mutations indicating the association between obesity and diabetes and the potential as future risk factors for pancreatic cancer, results confirming the applicability to diagnosis related to prognosis or prediction were derived; and, similar result patterns of genetic mutations inherited through the maternal line were confirmed, demonstrating the possibility of early diagnosis and preventive medical application for diseases that can genetically occur between family members in relation to chronic diseases such as diabetes.

9 FIG. As a result of analyzing somatic variants in three types of specimens nail, blood, and surgical tissue from bladder cancer patients, more bladder cancer-related somatic mutations were discovered in nail samples compared to whole blood. As shown in Table 6 andbelow, when comparing each mutation, it was found that the nail samples shared more common mutations with the tissue samples.

TABLE 6 Variant info Sample type Gene Ref Alt CDS change Tissue Nail Blood MSH6 CT C c.4002 − 10del o o o MSH6 AT A c.3557 − 4del o o PIK3CA CT C c.562 + 88del o o o TP53 G A c.574C > T o CDKN2A G T c.69C > A o ERBB2 G A c.1738G > A o EGFR C T c.241 − 26C > T o PMS2 GAA G c.706 − 5_706 − 4del o ERBB2 TA T o ERBB2 G A c.2209 − 93G > A o KRAS T G c.*3901A > C o EGFR G A c.402G > A o MLH1 AAC A c.1409 + 1226_1409 + o 1227del

Additionally, in the case of individual specific mutations detected by WES, the depth of coverage is relatively lower compared to targeted sequencing, and because the number of analyzed genes is large, variants of unknown significance (VOUS) may be detected; therefore, if a specific genetic disease is suspected, target genes and mutations can be secondary-screened through nail specimen-based targeted sequencing (Panels) targeting specific diseases, and then a panel kit can be manufactured to obtain high-depth data of 1000× or more.

In the report generation stage, the results delivered from the nail sample analysis operation and the results delivered from the clinical information analysis operation may be aggregated and provided in the form of an HTML5-based responsive web, which can be provided to terminals accessible by individuals in the case of frontline screening centers or local hospital DTC services. Each of these components may refer to software or hardware such as an FPGA (Field Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit), but is not limited to software or hardware; they may be configured to reside in an addressable storage medium or may be configured to execute one or more processors consisting of subdivided components or a plurality of components.

In addition, the data collected by the above method can be advanced through machine learning for big data, and supervised learning or unsupervised learning can be utilized depending on conditions; a process to increase reliability can be performed through supervised learning that optimizes variables related to diseases by adding clinical information (blood pressure, blood glucose, smoking, drinking, disease history, etc.), mutation information of chronic diseases or cancer genes, psychological questionnaire information, and gut microbiome information, under a state where specific answers exist. As more individual samples and clinical information are accumulated, they can be utilized as deep learning algorithms to increase accuracy and to perform correlation analysis between clinical findings and the discovery of mutant genes with accurate prognostic predictions for monitoring.

Based on such an analysis system, it is possible to provide customized checkup information or care methods according to diagnosis for each requester every month, based on uniquely stored individual information. For example, an online result report can be provided to the institution, checkup center, hospital, etc., where the requester submitted a nail sample, and the institution, checkup center, hospital, etc., provided with the online result report can grasp the requester's situation and recommend an intensive diagnosis if it is predicted that an additional examination is necessary, and can provide the requester with superior wellness-related counseling services, genetic health checkup counseling services, or medical services.